Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,371
datasets available to search
ShareScore release 0.7.1
Dataset results
2,371 results for “platform”
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,
Data of Benchmarking Elasticity of FaaS Platforms
<p>This data is part of the publication "Benchmarking Elasticity of FaaS Platforms as a Foundation for Objective-driven Design of Serverless Applications", it contains all plots and data used for the assessment of FaaS platform quality under volatile workloads from a client-side perspective. The paper is part of SAC'20, Brno, Czech Republic.</p>
augMENTOR: Simulated Student Learning Profiles and their Engagement Metrics in TryHackMe Platform_V1
<p>The dataset provides simulated insights into student engagement and performance within the THM platform. It outlines mathematical representations of student learning profiles, detailing behaviors ranging from high achievers to inconsistent performers. Additionally, the dataset includes key performance indicators, offering metrics like room completion, points earned, and time spent to gauge student progress and interaction within the platform's modules.</p><p>Here are definitions of the learning profiles, along with mathematical representations of their behaviors:</p><ul><li>High Achiever: These are students who consistently perform well across all modules. Their performance can be described as a normal distribution centered at a high mean value. Their performance P in a given module can be modelled as: P = N(90, 5) where N is the normal distribution function, 90 is the mean, and 5 is the standard deviation.</li><li>Average Performer: These are students who typically perform at the average level across all modules. Their performance can be described as a normal distribution centered at a medium mean value: P = N(70, 10), where 70 is the mean, and 10 is the standard deviation.</li><li>Late Bloomer: These are students whose performance improves as they progress through the modules. Their performance can be modelled as: P = N(50 + i*10, 10), where i is the module index and shows an increasing trend.</li><li>Specialized Talent: These are students who have average performance in most modules but excel in a particular module (e.g., module5). Their performance can be described as: P = N(90, 5) if the module is module 5, else P = N(70, 10).</li><li>Inconsistent Performer: These are students whose performance varies significantly across modules. Their performance can be described as a normal distribution with a high standard deviation: P = N(70, 30), where 70 is the mean, and 30 is the high standard deviation, reflecting inconsistency.</li></ul><p>Note that the actual performances are bounded between 0 and 100 using the function max(0, min(100, performance)) to ensure valid percentages.</p><p>In these formulas, the <i>np.random.normal</i> function is used to simulate the variability in student performance around the mean values. The first argument to this function is the mean, and the second argument is the standard deviation, reflecting the level of variability around the mean. The function returns a number drawn from the normal distribution described by these parameters. Note that the proposed method is experimental and has not been validated. </p><p> </p><p>List of Key Performance Indicators (KPIs) for Student Engagement and Progress within the Platform:</p><ul><li>Room Name: This represents the unique identifier or name of a specific room (or module). Think of each room as a separate module or lesson within an educational platform. For example, Room1, Room2, etc.</li><li>Total rooms completed: Indicates the cumulative number of rooms that a student has fully completed. Completion is typically determined by meeting certain criteria, like answering all questions or achieving a certain score.</li><li>Rooms registered in: Represents the number of rooms a student has registered or enrolled in. This could be different from the total number of rooms they've completed.</li><li>Ratio of Questions completed per room: This gives an insight into a student's progress in a particular room. For instance, a ratio of 7/10 suggests the student has completed 7 out of 10 available questions in that room.</li><li>Room Completed (yes no): Indicates whether a student has fully completed a specific room or not. This could be determined by the percentage of material covered, questions answered, or a certain score achieved.</li><li>Room Last deploy (count of days): Refers to the number of days since the last update or deployment was made to that room. It can give an idea about the effort of the student.</li><li>Points in room used for the leaderboard (range 0-560): Each room assigns points based on student performance, and these points contribute to leaderboards. The range suggests that a student can earn anywhere from 0 to 560 points in a particular room.</li><li>Last answered question in a room (27th Jan 2023): This indicates the date when a student last answered a question in a specific room. It can provide insights into a student's recent activity and engagement.</li><li>Total points in all rooms (range 0-560): The cumulative score a student has achieved across all rooms.</li><li>Path Percentage completed (range 0-100): Indicates the percentage of the overall learning path that the student has completed. A path could consist of multiple modules or rooms.</li><li>Module Percentage completed (range 0-100): Represents how much of a specific module (which could have multiple lessons or topics) a student has completed.</li><li>Room Percentage completed (range 0-100): Shows the percentage of a specific room that has been completed by a student.</li><li>Time Spent on the platform (seconds): This provides an aggregate of the total time a student has spent on the entire educational platform.</li><li>Time spent on each room (seconds): Represents the amount of time a student has dedicated to a specific room. This can give insights into which rooms or modules are the most time-consuming or engaging for students.</li></ul>
NMRduino: A modular, open-source, low-field magnetic resonance platform
<p>The NMRduino is a compact, cost-effective, sub-MHz NMR spectrometer that utilizes readily available open-source hardware and software components. One of its aims is to simplify the processes of instrument setup and data acquisition control to make experimental NMR spectroscopy accessible to a broader audience. In this introductory paper, the key features and potential applications of NMRduino are described to highlight its versatility both for research and education.</p>
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar.</p> <p>(The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
Massive IoT for Large-Scale Public Events in the 5GENESIS Surrey Platform
<p>This dataset contains the results of the trials conducted within the context of the main IoT use case of the 5GENESIS Surrey Platform.</p>
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
Experimental layout of pump-repump-probe on ELYSE platform
<p>This scheme represents the experimental layout designated for conducting electron pulse broad supercontinuum probe and pulse-repump-probe spectroscopy on linear electron accelerator ELYSE. </p>
QRNG module integrated on a polymer photonic-platform (polyboard)
<p>This dataset includes measured random number distribution and generated randomness evaluation results on the Polyboard QRNG module with 4 output paths (1x4) as well as characterization measurements of the Polyboard QRNG module with 16 output paths and integrated SPADs including dark-count rates and detector efficiency evaluation.</p>
FEL source input for simex_platform tutorial
<p>Simulated x-ray pulse for European XFEL SASE1 beamline 20pC bunch charge (ca. 3 fs pulse duration), undulator length nzc=35, 4.96 keV photon energy.</p> <p>To be used as input file for the simex_platform tutorial on single particle imaging (www.github.com/eucall-software/simex_platform/wiki/SimEx-Tutorial)</p>
Barley as a production platform for oral vaccines in sustainable fish aquaculture
<p>Experimental data for the study "Barley as a production platform for oral vaccines in sustainable fish aquaculture"</p>
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
Software developers are users of the Heureka microservice platform
<p>This data set contains the qualitative analysis of the "software developers are users" study, conducted to investigate the fitting of the Heureka microservice platform (http://doc.soteto.net) to support software developers in participating the change of socio-technical evolutionary-teal organizations.</p> <p>The study is part of the SOTETO project (http://soteto.net) and its results will be published and contextualized by the dissertation thesis of Johann Sell.</p>
Supplementary dataset to publication: "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens"
<p>The dataset supplements the journal article "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens" by H. Schomburg, J. Malchow, O. Sanders, J. Knöll and L. Schrader, that appeared in Smart Agricultural Technology 3 (2023), https://doi.org/10.1016/j.atech.2022.100095. The file archives trial1.zip and trial2.zip contain csv files with platform weighing system data measured from June 19, 2019 to July 22, 2019 (trial 1) and from September 9, 2019 to October 14, 2019 (trial 2) in a broiler chicken barn at Friedrich-Loeffler-Institut, Institute of Animal Welfare and Animal Husbandry, Celle. A detailed description of data structure is given in 00_hl_weighing_system_data_overview.txt.</p>
Darwin Core Archive: Santa Barbara Channel fish surveys at shallow regions of oil and gas platforms (SCUBA)
This dataset included fish counts that were surveyed in the shallow sections (0 – 40 meters) of the oil and gas platforms using Scuba. The oil and gas platforms are located in the Santa Barbara Channel, California, USA. Each of the eleven platforms (GILDA, GINA, HOLLY, IRENE, HERMOSA, HIDALGO, GAIL, GRACE, HARVEST, C, and HENRY) were surveyed multiple times a year from 1995 to 2000. This scuba project was conducted and reported under a cooperative agreement (Agreement 1445-CA09-95-0836) between the U. S. Geological Survey (Biological Resources Division) and the University of California, Santa Barbara. This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. Citation of report: Love, M. S., D. M. Schroeder, and M. M. Nishimoto. 2003. The ecological role of oil and gas production platforms and natural outcrops on fishes in southern and central California: a synthesis of information. U. S. Department of the Interior, U. S. Geological Survey, Biological Resources Division, Seattle, Washington, 98104, OCS Study MMS 2003-032. http://www.lovelab.id.ucsb.edu/Report.pdf These fish surveys at platforms were conducted within a few days of surveys at rock outcrops, which can be viewed at: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=112 The deeper sections of the oil platform were surveyed using the research submarine Delta; the data can be viewed at: https://portal. edirepository.edu/nis/mapbrowse?scope=edi&identifier=111
2012 climate data for eddy flux platform on Toolik Lake, Alaska
Yearly file describing the metological conditions on Toolik Lake adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This location is a floating platform where eddy flux measurements have been made, and should not be confused with either the Toolik Field Station Climate site, which is a land-based station, or the Toolik Lake Climate Station that is lake-based but at a different location (approximately 300 m from the eddy platform). Note that the terrestrial station has been called the "Toolik Main Climate Station", and the station on the lake is located near the main Arctic long term ecological research lake sampling site, and has also been called the Toolik Lake Main Climate Station. Measurements at the eddy climate platform described here include air temperature, relative humidity, barometric pressure, wind direction, wind speed, radiation, and water temperature.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.